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A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
telegram-owl — это лёгкий и удобный инструмент командной строки (CLI), написанный на языке Go, который позволяет отправлять текстовые сообщения, файлы и мультимедиа в Telegram-каналы, чаты и группы прямо из терминала.
Этот инструмент будет полезен:
- разработчикам
- DevOps-инженерам
- системным администраторам
- авторам скриптов автоматизации
- всем, кто хочет получать уведомления в Telegram из своих программ или серверов
Freebie for the EYES keyword (reel-47, "Give Claude eyes"). Deliver as a public GitHub Gist — numbered steps the user can run today, not a raw link. Value-first: by the end you have a Claude Code skill that sees a video (every cut, every on-screen detail), not just reads its transcript.
Claude has no native video model. So every "analyze this video" tool just pulls the transcript — and
| import type { Plugin } from "@opencode-ai/plugin" | |
| export default (async () => ({ | |
| "chat.headers": async (input, output) => { | |
| if (input.model.providerID !== "openai") return | |
| output.headers.originator = "codex_cli_rs" | |
| output.headers["User-Agent"] = "codex_cli_rs/0.0.0 (OpenCode)" | |
| }, | |
| })) satisfies Plugin |
You can use the following commands to limit the maximum power allowed to your NVIDIA GPU when using linux systems.
First, let's check how much power the GPU is allowed to draw and the current value:
nvidia-smi -q -d POWERThis should return an output similar to this one:
I’ve written a newer optimized 2026 version of this guide with a faster setup using Zinit + Starship and improved plugin loading.
This version avoids slow shell startups and works better with modern Zsh setups.
Updated guide: https://gist.github.com/n1snt/2cccc8aa5f7b645a7628d3512c70deb6
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| all: clean build run | |
| build: | |
| gcc -nostdlib -Wall -shared -fPIC hello_exit.S -Wl,-soname,libhelloexit.so.1 -o libhelloexit.so.1.0 | |
| ln -sv libhelloexit.so.1.0 libhelloexit.so.1 | |
| ln -sv libhelloexit.so.1.0 libhelloexit.so | |
| gcc -nostdlib -Wall -L. -I. hello.c -lhelloexit -Wl,-rpath,. -o hello | |
| @echo Done | |
| run: |
